Image processing apparatus, image processing method, and non-transitory computer readable storage medium

ABSTRACT

An image processing obtains a plurality of measurement data from a measurement apparatus for obtaining the plurality of measurement data of an object, divides the plurality of measurement data into a plurality of subsets, distributes the measurement data included in the plurality of subsets to operation units in each repetitive operation, divides an image region into a plurality of regions, distributes the plurality of regions to the operation units, updates a result obtained by each operation unit in the distributed region information using the distributed measurement data, thereby performing the reconstruction process and generating a partial reconstructed image on each operation unit, and combines the partial reconstructed images to generate the reconstructed image.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image reconstruction process of animage diagnostic apparatus that forms a tomographic image in an object.

2. Description of the Related Art

An image diagnostic apparatus that forms a tomographic image in anobject forms a tomographic image using radiation. The tomographic imageis used for diagnosis of a patient by a doctor or the like. Such animage diagnostic apparatus performs an image reconstruction process toobtain the tomographic image. An image diagnostic apparatus such as anX-ray computed tomography (CT) apparatus, a positron emission tomography(PET) apparatus, or a single photon emission CT (SPECT) apparatusincludes projection calculation in the image reconstruction process.

Image reconstruction processing methods including projection calculationare roughly divided into analytical methods and successiveapproximation. In the analytical methods, the processing load is light,but the quality of a reconstructed image is low. In the successiveapproximation, although the processing load is heavy, the image qualitycan be expected to be improved by reducing noise on a reconstructedimage. One of the successive approximation image reconstruction methodsis a block iteration type successive approximation image reconstructionmethod.

Conventionally, when performing the image reconstruction process using aplurality of operation units, measurement data (tomogram data of anobject) obtained by the image diagnostic apparatus is distributed amongthe operation units, and after that, the plurality of operation unitsperform parallel processes, as described in Japanese Patent Laid-OpenNo. 2011-72827. In this case, if an image region (image space) to becalculated can be specified from the measurement data, only the imageregion is distributed among the operation units. If the image regioncannot be specified, the data of the whole image region is distributedamong the operation units. In addition, to shorten the data transfertime among the operation units, divided images are distributed to theoperation units, and parallel processes are performed, as described inZakaria Bahi, Julien Bert, Awen Autret and Dimitris Visvikis, “HighPerformance Multi-GPU Acceleration for Fully 3D List-Mode PETReconstruction”, 2012 IEEE Nuclear Science Symposium and Medical ImagingConference Record, 2012.

When forming a detailed tomographic image in an object, measurement data(tomogram data of the object) is obtained in a large scale. Hence, animage reconstruction processing method for handling the large-scalemeasurement data using a plurality of operation units is indispensable.However, since an image reconstruction process using the large-scalemeasurement data or reconstructed image data is performed, the dataamount exceeds the memory size of the operation units.

SUMMARY OF THE INVENTION

The present invention has been made in consideration of the aboveproblem, and has as its object to implement an image reconstructionmethod for handling large-scale data.

According to one aspect of the present invention, there is provided animage processing apparatus for generating a reconstructed image byperforming a reconstruction process including a repetitive operationusing a plurality of operation units, which comprises: an obtaining unitconfigured to obtain a plurality of measurement data from a measurementapparatus for obtaining the plurality of measurement data of an object;a first division unit configured to divide the plurality of measurementdata into a plurality of subsets; a first distribution unit configuredto distribute the measurement data included in the plurality of subsetsto the operation units in each repetitive operation; a second divisionunit configured to divide an image region into a plurality of regions; asecond distribution unit configured to distribute the plurality ofregions to the operation units; a reconstruction processing unitconfigured to update a result obtained by each operation unit in thedistributed region information using the distributed measurement data,thereby performing the reconstruction process and generating a partialreconstructed image on each operation unit; and a combining unitconfigured to combine the partial reconstructed images to generate thereconstructed image.

According to another aspect of the present invention, there is providedan image processing apparatus for generating a reconstructed image byperforming a reconstruction process including a repetitive operationusing a plurality of operation units, which comprises: a measurementdata division unit configured to divide measurement data that istomogram data of an object; a measurement data distribution unitconfigured to distribute the measurement data divided by the measurementdata division unit to the operation units; an image data division unitconfigured to divide image data that is volume data of the object; animage data distribution unit configured to distribute the image datadivided by the image data division unit to the operation units; areconstruction processing unit configured to perform, in the operationunits, reconstruction processes including repetitive operations inparallel using the measurement data and the image data distributed tothe operation units to generate a partial reconstructed image; and acombining unit configured to combine the partial reconstructed imagesgenerated by the operation units.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments (with reference to theattached drawings).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram showing an example of thearrangement of an image processing apparatus 100;

FIG. 2 is a view for explaining operations of a reconstruction processaccording to the first embodiment;

FIGS. 3A and 3B are flowcharts showing an example of a process formexecuted by the image processing apparatus 100;

FIG. 4 is a schematic view of an image diagnostic apparatus according tothe first embodiment;

FIG. 5 is a conceptual view of processes according to the firstembodiment;

FIG. 6 is a schematic view of an image diagnostic apparatus according tothe second embodiment; and

FIG. 7 is a conceptual view of processes according to the secondembodiment.

DESCRIPTION OF THE EMBODIMENTS

Hereinafter, embodiments of the present invention will be described indetail with reference to the accompanying drawings. It should be notedthat the following embodiments are not intended to limit the scope ofthe appended claims, and that not all the combinations of featuresdescribed in the embodiments are necessarily essential to the solvingmeans of the present invention. Note that the same parts will bedescribed by adding the same reference numerals.

First Embodiment

FIG. 1 is a functional block diagram showing an example of thearrangement of an image processing apparatus 100 according to the firstembodiment. The image processing apparatus 100 performs reconstructionprocesses including repetitive operations in parallel using a pluralityof operation units to generate a reconstructed image. Main parts of theimage processing apparatus 100 will be described below. The imageprocessing apparatus may include other processing units. An imagereconstruction processing unit 101 is formed from a plurality ofoperation units and performs an image reconstruction process. Morespecifically, the image reconstruction processing unit 101 updates aresult obtained by each operation unit in distributed region informationusing distributed measurement data, thereby performing a reconstructionprocess and generating a partial reconstructed image on each operationunit. Details will be described later. Each operation unit incorporatesa storage memory. The storage memory stores distributed measurement data(tomogram data of an object) (to be described later) and image data.

Measurement data are data obtained by measuring tomograms of an objectfrom different angles using radiation. On the other hand, image data isvolume data that can be obtained as a result of the image reconstructionprocess according to this embodiment. As initial image data, forexample, data generated by substituting a predetermined value in a rangeto be subjected to imaging and 0 in a range not to be subjected toimaging is used, as will be described later.

Operation processes by the operation units are performed in parallel.The operation units operate on GPUs or CPUs. Note that the operationunits used are not limited to those operating on GPUs or CPUs. Anoperation control unit 109 performs a process of aggregating/adding theresults of the image reconstruction processes executed by the pluralityof operation units in the image reconstruction processing unit 101 anddistributing the result to the operation units. Note that the operationcontrol unit 109 is not limited to that operating on a GPU or CPU.

A measurement data storage unit 102 obtains a plurality of measurementdata (tomogram data of an object) from a tomogram measurement apparatusthat obtains the plurality of measurement data from the object andstores the measurement data. FIG. 4 illustrates the arrangement of thetomogram measurement apparatus according to this embodiment. Thetomogram measurement apparatus obtains a plurality of measurement data.As shown in FIG. 4, the tomogram measurement apparatus is a computedtomography (CT) apparatus that is a medical image diagnostic apparatusincluding an X-ray tube 401 configured to irradiate an object withX-rays and a detector 402 configured to detect the X-rays that haveirradiated the object, in which the X-ray tube 401 and the detector 402are arranged so as to sandwich an image space 403 where imaging of theobject is performed. Note that the shape of the X-rays emitted by theX-ray tube 401 is not limited to a specific one and can be a parallelbeam, conical beam, or fan beam. The detector 402 is not limited to aflat detector as shown in FIG. 4.

A measurement data division unit 103 divides the measurement data thatare the tomogram data of the object obtained by the tomogram measurementapparatus and stored in the measurement data storage unit 102 into aplurality of subsets. Each subset includes measurement data to be usedby an operation unit in one iteration of the repetitive operation. Ameasurement data distribution unit 104 distributes the measurement data(divided measurement data) included in the subsets to the operationunits in each repetitive operation. In this embodiment, the measurementdata distribution unit 104 distributes identical data to the operationunits.

An initial image generation unit 105 generates first image data. Thatis, the initial image generation unit 105 generates an initial image asan input image to the image reconstruction process. The initial image isgenerated by substituting a predetermined value in a range (imagingrange) to be subjected to imaging and 0 in a range (a range outside theimaging range) not to be subjected to imaging in an imaging enable range(image region). As the value substituted in the imaging range, forexample, a uniform value or a value calculated from measurement data issubstituted. The method of calculating the value to be substituted isnot limited to a specific method. An image data storage unit 106 stores,as image data, the initial image generated by the initial imagegeneration unit 105 as the initial value. After the start of operationsby the plurality of operation units, the image data storage unit 106 maystore a reconstructed image generated by a reconstructed image combiningunit 110 every time the image reconstruction process is performed.

An image data division unit 107 divides the image data (image region) asthe volume data of the object into a plurality of regions as many as thenumber of operation units. At this time, the image data division unit107 performs the division process such that the divided image data forma continuous region in a predetermined range. An image data distributionunit 108 distributes the divided image data (divided region information)to the operation units. The image data distribution unit 108 distributesdifferent image data (a plurality of regions divided from the imageregion) to the operation units. The reconstructed image combining unit110 combines the reconstructed images generated by the imagereconstruction processes on the operation units.

FIG. 2 is a view for explaining operations of the reconstruction processaccording to this embodiment. An operation unit 101-A performs aregularization term operation 200-A, a forward projection operation201-A, a back projection operation 205-A, and an image updatingoperation 206-A. Similarly, operation units 101-B to 101-N performregularization term operations 200-B to 200-N, forward projectionoperations 201-B to 201-N, back projection operations 205-B to 205-N,and image updating operations 206-B to 206-N, respectively. Theoperation control unit 109 performs forward projection operation resultaggregation 202, forward projection operation result addition 203, andforward projection operation result distribution 204.

In the regularization term operations 200-A to 200-N, regularizationterms are calculated. The regularization term is a value obtained byexecuting a back projection operation before an iterative process of thesubsequent stage. In the forward projection operations 201-A to 201-N,forward projection operations are performed. The forward projectionoperation is a calculation of obtaining the sum of pixel values on aprojection line.

In the forward projection operation result aggregation 202, forwardprojection operation results obtained by the forward projectionoperations 201-A to 201-N are aggregated. This aggregation process isexecuted by the operation control unit 109.

In the forward projection operation result addition 203, all the forwardprojection operation results aggregated in the forward projectionoperation result aggregation 202 are added. More specifically, in theforward projection operation result addition 203, the forward projectionoperation results obtained by the operation units 101-A to 101-N areadded to form entire image data because the forward projection operationresults correspond to different parts of image data, as will bedescribed later. This addition process is executed by the operationcontrol unit 109.

In the forward projection operation result distribution 204, the forwardprojection operation result added by the forward projection operationresult addition 203 is distributed to the operation units 101-A to101-N. This distribution process is executed by the operation controlunit 109. More specifically, the added forward projection operationresult is divided according to partial image data corresponding to theprocess results (the forward projection operation results beforeaddition) of the operation units 101-A to 101-N and distributed.

In the back projection operations 205-A to 205-N, back projectionoperations are performed. More specifically, the back projectionoperation is a calculation of adding projection values. In the imageupdating operations 206-A to 206-N, operations for image updating areperformed. More specifically, in the image updating operations 206-A to206-N, back projection operation results obtained so far and the backprojection operation results obtained this time are multiplied. Afterthe end of this process, the process returns to the forward projectionoperations 201-A to 201-N, and an iterative process is performed.

FIGS. 3A and 3B are flowcharts showing an example of a process formexecuted by the image processing apparatus 100. FIG. 5 is a conceptualview of processes according to this embodiment, which will be describedtogether with the process shown in FIGS. 3A and 3B. FIGS. 3 and 5 showan example in which the number (=N) of operation units is 4. In stepS300, the measurement data division unit 103 divides measurement datastored in the measurement data storage unit 102 into S subsets. Eachsubset includes a data group to be used for calculation of oneiteration. When dividing measurement data into subsets, the measurementdata division unit 103 designates a specific view (projection angle) anddivides the measurement data into subsets. That is, the measurement datadivision unit 103 divides a plurality of measurement data into aplurality of subsets such that each of the plurality of subsets includesmeasurement data of different projection angles. The specific view canarbitrarily be determined. In this embodiment, a projection angle indexcorresponding to a projection angle of irradiation is assumed to beadded to each measurement data. The measurement data division unit 103designates a projection angle index to be included in each subsetwithout dividing the detection data of each projection angle, therebydividing the plurality of measurement data into the plurality ofsubsets.

In the example shown in FIG. 5, the number of detection data included inmeasurement data 50 is 12, and the number (=S) of subsets is 4. At thistime, the measurement data division unit 103 divides detection data ofprojection angle indices 1, 5, and 9 into a subset 50-1, detection dataof projection angle indices 2, 6, and 10 into a subset 50-2, detectiondata of projection angle indices 3, 7, and 11 into a subset 50-3, anddetection data of projection angle indices 4, 8, and 12 into a subset50-4. Note that the method of designating the projection angle indicesfor division is not limited to the above example. In this way, themeasurement data division unit 103 designates the projection angleindices and performs division, thereby dividing the measurement datawithout adding a process.

In step S301, the initial image generation unit 105 generates initialimage data as an input image to the image reconstruction process. Inthis embodiment, the predetermined value to be substituted in a range(whole image region) to be subjected to imaging as the initial image isa uniform value calculated from the measurement data stored in themeasurement data storage unit 102. Note that the value to be substitutedis not limited to the above value.

In step S302, the image data division unit 107 performs an image datadivision process. At this time, the image data division unit 107 dividesan image region into a plurality of regions such that each region has apredetermined continuous range. In addition, the image data divisionunit 107 divides the image region into a plurality of regions having thesame range. Note that the division method at this time is not limited toa specific method. For example, in the image space, the horizontaldirection in a slice section is defined as an X-axis direction, thevertical direction is defined as a Y-axis direction, and the slicedirection is defined as a Z-axis direction. At this time, a divisionmethod of dividing an image by planes parallel to the X-axis direction(image data 51 is divided into image data 51-1 to 51-4), as shown inFIG. 5, is usable. Various other division methods such as dividing animage by planes parallel to the Y-axis direction or Z-axis direction anddividing an image in an oblique direction with respect to each axis arealso usable. The divided regions need not be uniform among the operationunits and can arbitrarily be determined. Note that in FIGS. 3A and 3B,the process advances from step S300 sequentially to steps S301 and S302.However, the process of step S300 and those of steps S301 and S302 maybe replaced with each other.

The reason why an image reconstruction process for large-scale data thatcannot be held on an operation unit can be performed will be describedhere in detail. When the image size is 1024×1024×1024 pixels, and anumerical value is handled as a float type, the amount of image data isabout 4.3 GB. When the size of measurement data corresponding to thisimage size is 2048×1024×1024, and a numerical value is handled as afloat type, the amount of the measurement data is about 8 GB.

As arrays that need to be ensured to calculate the image reconstructionmethod, there are an array that holds forward projection operationresults, a regularization term array, and an array that holds backprojection operation results in addition to the arrays of reconstructedimages and measurement data. In this embodiment, the texture function ofa GPU is used for high-speed implementation. Hence, two arrays of inputreconstructed images and output reconstructed images are prepared forreconstructed images. Accordingly, two arrays for the measurement datasize and four arrays for the reconstructed image size are needed intotal. If all data are distributed to the operation units without beingdivided, a memory size of 30 GB or more is necessary. When the imagereconstruction process is implemented by using four GPUs for theoperation units and setting the number of subsets to 16, the sizes ofdata distributed to each operation unit are about 1.07 GB for dividedimage data and 0.5 GB for divided measurement data. Hence, the data sizedistributed to the GPU can be decreased to about 5.28 GB in total, andthe data can be held on the memory of the GPU.

Referring back to FIGS. 3A and 3B, in steps S303A to S303D, the imagedata distribution unit 108 distributes the image data divided in stepS302 to the operation units. This process corresponds to distributingthe image data 51-1 to 51-4 divided from the image data 51 to operationunits 101-1 to 101-4 in FIG. 5.

Next, the image reconstruction processing unit 101 performs theprocesses of steps S304 to S311, that is, the iterative calculationprocesses shown in FIG. 2. First, in steps S304A to S304D, the operationunits 101-1 to 101-4 of the image reconstruction processing unit 101execute the regularization term operations 200-A to 200-4. Next, insteps S305A to S305D, the measurement data distribution unit 104distributes the measurement data divided in step S300 to the operationunits. This process corresponds to distributing the detection data ofprojection angle index 1 of the subset 50-1 to the operation units 101-1to 101-4 for one iterative process in FIG. 5. In steps S306A to S306D,the operation units 101-1 to 101-4 execute the forward projectionoperations 201-A to 201-4.

In step S307, the operation control unit 109 executes the forwardprojection operation result aggregation 202, the forward projectionoperation result addition 203, and the forward projection operationresult distribution 204. In steps S308A to S308D, the operation units101-1 to 101-4 execute the back projection operations 205-A to 205-4. Insteps S309A to S309D, the operation units 101-1 to 101-4 execute theimage updating operations 206-A to 206-4.

The processes of steps S305 to S309 in FIGS. 3A and 3B correspond to animage reconstruction process 53 in FIG. 5. The procedure of the imagereconstruction process will be described with reference to FIG. 5.First, each of the operation units 101-1 to 101-4 performs one iterativeprocess (image reconstruction process 1) for the detection data ofprojection angle index 1 out of the subset 50-1 to obtain a backprojection result. Next, each of the operation units 101-1 to 101-4performs one iterative process (image reconstruction process 1) for thedetection data of projection angle index 5, and multiplies the backprojection result obtained in this process by the back projection resultobtained in the preceding process. Then, each of the operation units101-1 to 101-4 performs one iterative process (image reconstructionprocess 1) for the detection data of projection angle index 9, andmultiplies the back projection result obtained in this process by theback projection result obtained in the preceding process. After theiterative processes for the subset 50-1 have ended, the process targetshifts to the subset 50-2. The iterative processes (image reconstructionprocesses 2) are performed for a plurality of projection angle indices,as in the subset 50-1.

In step S310, the operation control unit 109 determines the end of thesubset calculation loop. If the loop has ended, the process advances toiterative calculation end determination in step S311. If the loop hasnot ended, the process returns to steps S305A to S305D to repeat theprocess. In step S311, the operation control unit 109 determines the endof iterative calculation. If the iterative calculation has ended, theprocess advances to a reconstructed image combining process in stepS312. If the iterative calculation has not ended, the process returns tosteps S305A to S305D to repeat the process. In the example of FIG. 5, ifthe end condition is not met after the end of the process up to thesubset 50-4, image reconstruction process 1 is performed again. In thisway, using the measurement data and image data distributed to theoperation units, the image reconstruction processing unit 101 performsthe image reconstruction processes including the repetitive operationsin parallel in the operation units, thereby generating partialreconstructed images. In step S312, the reconstructed image combiningunit 110 combines the partial reconstructed images generated by finallyupdating the distributed image data (distributed region information) inthe operation units, thereby generating a reconstructed image.

In this embodiment, by performing such process control, a CT imagereconstruction process for large-scale data can be implemented using aplurality of operation units. Since each operation unit can save onlypartial detection data divided by the projection angle and perform theiterative operation without saving all detection data that constructmeasurement data in a memory, the effect of decreasing the memorycapacity can also be obtained.

Second Embodiment

In the first embodiment, a method of designating measurement data ofeach projection angle and dividing the measurement data in the imagereconstruction process of a CT apparatus has been described. In thisembodiment, concerning the image reconstruction process of a positronemission tomography (PET) apparatus that is a medical image diagnosticapparatus, an embodiment for measurement data having a list mode dataformat with time-serially recorded detection events (conditions fordetection) will be described.

FIG. 6 illustrates a tomogram measurement apparatus according to thisembodiment. As shown in FIG. 6, the tomogram measurement apparatusaccording to this embodiment is a PET apparatus with detectors arrangedso as to surround an object. One detector is arranged to detectradiation emitted by an opposing detector, thereby obtaining measurementdata. Note that the detector arrangement pattern is not limited to thecircular pattern shown in FIG. 6.

FIG. 7 is a conceptual view of processes according to this embodiment.Note that the image reconstruction process shown in FIG. 1, the imagereconstruction means shown in FIG. 2, and the image reconstructionprocess procedure shown in FIGS. 3A and 3B are the same as in the firstembodiment, and a description thereof will be omitted. Points differentfrom the first embodiment will mainly briefly be described. In thisembodiment, a radiation detection time, the addresses of a detector pair(the positions of a pair of detectors), and the energy value of detectedradiation are recorded in each measurement data as a detection event(list mode data), unlike the first embodiment. A measurement datadivision unit 103 designates a detection event (event number), therebydividing a plurality of measurement data into a plurality of subsets. Inthe example of FIG. 7, measurement data 70 are divided into subsetssequentially from the upper side (sequentially from 0). That is, themeasurement data division unit 103 divides a plurality of measurementdata into a plurality of subsets such that each of the plurality ofsubsets includes measurement data obtained under different conditions.Note that the subset division method is not limited to this. Forexample, event numbers may be designated at a predetermined interval. Ameasurement data distribution unit 104 distributes the measurement datadivided into subsets by designating event numbers to the operationunits, as in the first embodiment.

A flowchart showing an example of a process form executed by an imageprocessing apparatus 100 according to this embodiment is shown in FIGS.3A and 3B, as in the first embodiment. In this embodiment, the processesof steps S305 to S309 of FIGS. 3A and 3B correspond to an imagereconstruction process 73 shown in FIG. 7. The procedure of the imagereconstruction process will be described with reference to FIG. 7.First, each of operation units 101-1 to 101-4 performs one iterativeprocess (image reconstruction process 1) for the data of event number 0out of a subset 70-1 to obtain a back projection result. Next, each ofthe operation units 101-1 to 101-4 performs one iterative process (imagereconstruction process 1) for the data of event number 1, and multipliesthe back projection result obtained in this process by the backprojection result obtained in the preceding process. Such a process isperformed as many times as the number of event numbers included in thesubset 70-1. After the iterative processes for the subset 70-1 haveended, the process target shifts to a subset 70-2. The iterativeprocesses (image reconstruction processes 2) are performed, as in thesubset 70-1. In the example of FIG. 7, if the end condition is not metafter the end of the process up to a subset 70-N (step S311), imagereconstruction process 1 is performed again.

The reason why an image reconstruction process for large-scale data thatcannot be held on an operation unit can be performed will be describedhere in detail. The image size is assumed to be the same as in the firstembodiment. On the other hand, measurement data is assumed to have asize corresponding to 500M events. A radiation detection time, theaddresses of a detector pair (the positions of a pair of detectors), andthe energy value of detected radiation are recorded in each event. Whena numerical value is handled as an int type, the amount of themeasurement data is about 6 GB. Arrays necessary to calculate asuccessive approximation image reconstruction method are two arrays forthe measurement data size and four arrays for the reconstructed imagesize, as in the first embodiment. Hence, if all data are distributed tothe operation units without being divided, a memory size of 29 GB ormore is necessary. When the successive approximation imagereconstruction method is implemented by using four GPUs for theoperation units and setting the number of subsets to 8, the sizes ofdata distributed to each operation unit are about 1.07 GB for dividedimage data and 0.75 GB for divided measurement data. Hence, the datasize distributed to the GPU can be decreased to about 5.78 GB in total,and the data can be held on the memory of the GPU.

Note that in this embodiment, a PET image reconstruction process usinglist mode data has been described. When a histogram mode data in whichthe number of detection events of each detector pair is recorded isused, the measurement data division unit 103 divides measurement data bydesignating projection angles as described in the first embodiment.

In this embodiment, by performing such process control, a PET imagereconstruction process for large-scale data that cannot be held onoperation units can be implemented using a plurality of operation units.

OTHER EMBODIMENTS

Embodiment(s) of the present invention can also be realized by acomputer of a system or apparatus that reads out and executes computerexecutable instructions (e.g., one or more programs) recorded on astorage medium (which may also be referred to more fully as a‘non-transitory computer-readable storage medium’) to perform thefunctions of one or more of the above-described embodiment(s) and/orthat includes one or more circuits (e.g., application specificintegrated circuit (ASIC)) for performing the functions of one or moreof the above-described embodiment(s), and by a method performed by thecomputer of the system or apparatus by, for example, reading out andexecuting the computer executable instructions from the storage mediumto perform the functions of one or more of the above-describedembodiment(s) and/or controlling the one or more circuits to perform thefunctions of one or more of the above-described embodiment(s). Thecomputer may comprise one or more processors (e.g., central processingunit (CPU), micro processing unit (MPU)) and may include a network ofseparate computers or separate processors to read out and execute thecomputer executable instructions. The computer executable instructionsmay be provided to the computer, for example, from a network or thestorage medium. The storage medium may include, for example, one or moreof a hard disk, a random-access memory (RAM), a read only memory (ROM),a storage of distributed computing systems, an optical disk (such as acompact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™),a flash memory device, a memory card, and the like.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2015-083706, filed Apr. 15, 2015, which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. An image processing apparatus for generating areconstructed image by performing a reconstruction process including arepetitive operation using a plurality of operation units, comprising:an obtaining unit configured to obtain a plurality of measurement datafrom a measurement apparatus for obtaining the plurality of measurementdata of an object; a first division unit configured to divide theplurality of measurement data into a plurality of subsets; a firstdistribution unit configured to distribute the measurement data includedin the plurality of subsets to the operation units in each repetitiveoperation; a second division unit configured to divide an image regioninto a plurality of regions; a second distribution unit configured todistribute the plurality of regions to the operation units; areconstruction processing unit configured to update a result obtained byeach operation unit in the distributed region information using thedistributed measurement data, thereby performing the reconstructionprocess and generating a partial reconstructed image on each operationunit; and a combining unit configured to combine the partialreconstructed images to generate the reconstructed image.
 2. Theapparatus according to claim 1, wherein the first division unit dividesthe plurality of measurement data into the plurality of subsets suchthat each of the plurality of subsets includes measurement data ofdifferent projection angles.
 3. The apparatus according to claim 2,wherein a projection angle index corresponding to the projection angleis added to each measurement data, and the first division unitdesignates the projection angle index, thereby dividing the plurality ofmeasurement data into the plurality of subsets.
 4. The apparatusaccording to claim 2, wherein the image processing apparatus comprises acomputed tomography (CT) apparatus.
 5. The apparatus according to claim1, wherein the measurement apparatus comprises a plurality of detectionunits arranged so as to surround the object, one detection unit out ofthe plurality of detection units arranged to face each other to detectradiation emitted by an opposing detection unit, thereby obtaining themeasurement data, and the first division unit divides the plurality ofmeasurement data into the plurality of subsets such that each of theplurality of subsets includes measurement data obtained under differentconditions.
 6. The apparatus according to claim 5, wherein a radiationdetection time, positions of a pair of detection units, and an energyvalue of detected radiation are recorded in each measurement data as adetection event, and the first division unit designates the detectionevent, thereby dividing the plurality of measurement data into theplurality of subsets.
 7. The apparatus according to claim 5, wherein theimage processing apparatus comprises a positron emission tomography(PET) apparatus.
 8. The apparatus according to claim 1, wherein thesecond division unit divides the image region into the plurality ofregions such that each region has a predetermined continuous range. 9.The apparatus according to claim 1, wherein the second division unitsdivides the image region into the plurality of regions having the samerange.
 10. An image processing apparatus for generating a reconstructedimage by performing a reconstruction process including a repetitiveoperation using a plurality of operation units, comprising: ameasurement data division unit configured to divide measurement datathat is tomogram data of an object; a measurement data distribution unitconfigured to distribute the measurement data divided by the measurementdata division unit to the operation units; an image data division unitconfigured to divide image data that is volume data of the object; animage data distribution unit configured to distribute the image datadivided by the image data division unit to the operation units; areconstruction processing unit configured to perform, in the operationunits, reconstruction processes including repetitive operations inparallel using the measurement data and the image data distributed tothe operation units to generate a partial reconstructed image; and acombining unit configured to combine the partial reconstructed imagesgenerated by the operation units.
 11. An image processing method ofgenerating a reconstructed image by performing a reconstruction processincluding a repetitive operation using a plurality of operation units,comprising: obtaining a plurality of measurement data from a measurementapparatus for obtaining the plurality of measurement data of an object;dividing the plurality of measurement data into a plurality of subsets;distributing the measurement data included in the plurality of subsetsto the operation units in each repetitive operation; dividing an imageregion into a plurality of regions; distributing the plurality ofregions to the operation units; updating a result obtained by eachoperation unit in the distributed region information using thedistributed measurement data, thereby performing the reconstructionprocess and generating a partial reconstructed image on each operationunit; and combining the partial reconstructed images to generate thereconstructed image.
 12. An image processing method of generating areconstructed image by performing a reconstruction process including arepetitive operation using a plurality of operation units, comprising:dividing measurement data that is tomogram data of an object;distributing the measurement data divided in the dividing themeasurement data to the operation units; dividing image data that isvolume data of the object; distributing the image data divided in thedividing the image data to the operation units; performing, in theoperation units, reconstruction processes including repetitiveoperations in parallel using the measurement data and the image datadistributed to the operation units to generate a partial reconstructedimage; and combining the partial reconstructed images generated by theoperation units.
 13. A non-transitory computer readable medium storing acomputer-executable program for causing a computer to perform an imageprocessing method of generating a reconstructed image by performing areconstruction process including a repetitive operation using aplurality of operation units, the method comprising: obtaining aplurality of measurement data from a measurement apparatus for obtainingthe plurality of measurement data of an object; dividing the pluralityof measurement data into a plurality of subsets; distributing themeasurement data included in the plurality of subsets to the operationunits in each repetitive operation; dividing an image region into aplurality of regions; distributing the plurality of regions to theoperation units; updating a result obtained by each operation unit inthe distributed region information using the distributed measurementdata, thereby performing the reconstruction process and generating apartial reconstructed image on each operation unit; and combining thepartial reconstructed images to generate the reconstructed image.
 14. Anon-transitory computer readable medium storing a computer-executableprogram for causing a computer to perform an image processing method ofgenerating a reconstructed image by performing a reconstruction processincluding a repetitive operation using a plurality of operation units,the method comprising: dividing measurement data that is tomogram dataof an object; distributing the measurement data divided in the dividingthe measurement data to the operation units; dividing image data that isvolume data of the object; distributing the image data divided in thedividing the image data to the operation units; performing, in theoperation units, reconstruction processes including repetitiveoperations in parallel using the measurement data and the image datadistributed to the operation units to generate a partial reconstructedimage; and combining the partial reconstructed images generated by theoperation units.